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In this paper, we present an adaptive learning (specifically, learning automata) Like (LAL) mechanism for congestion avoidance in wired networks. Our algorithm, named as learning automata like random early detection (LALRED), is founded on the principles of operations of the existing random early detection (RED) congestion avoidance mechanisms, augmented with a LAL philosophy. Our approach helps to improve the performance of congestion avoidance by adaptively minimizing the queue loss rate and the average queue size. Simulation results obtained using NS2 establish the improved performance of LALRED over the traditional RED, which was chosen as the benchmark for performance comparison purposes.